Power distribution network carbon emission metering and responsibility distribution method, equipment and medium

Abstract

The invention discloses a power distribution network carbon emission metering and responsibility distribution method and device and a medium, and relates to the technical field of smart power grids. The method comprises the following steps: collecting multi-source carbon emission data in real time, and preprocessing the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain a dynamic carbon factor of the thermal power generating unit, and combining the dynamic carbon factor of the thermal power generating unit with a carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; performing load flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch load flow distribution matrix, and combining the dynamic carbon intensity vector, the node active flux matrix and the branch load flow distribution matrix to obtain the total carbon potential of each node on the load side; and based on the total carbon potential and the real-time load distribution matrix, calculating the carbon flow rate distributed to each node on the load side so as to obtain the carbon emission responsibility of each node on the load side.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device, and medium for carbon emission measurement and responsibility allocation in a distribution network. Background Art

[0002] In the field of carbon emission measurement of distribution networks, the traditional approach is to use a fixed carbon emission factor based on regional or national averages, dividing the total carbon emissions on the power generation side by the total grid-connected power to obtain a unified carbon emission coefficient per unit of power (for example: / kWh ) and applies it to the electricity consumption calculations of all users. While this method is simple and easy to implement, it cannot reflect the real-time dynamic changes in the generation structure. In particular, with the large-scale integration of intermittent renewable energy sources such as distributed photovoltaics, the carbon intensity of the power grid fluctuates dramatically over time. The fixed factor method cannot capture these minute- or hour-by-minute changes. Secondly, this method cannot distinguish the true carbon intensity of electricity consumed by users at different locations in the grid. All users bear the same unit carbon cost, ignoring the impact of grid power flow distribution, resulting in unfair distribution of responsibilities. Traditional life cycle assessment (LCA) methods are mostly static models that are difficult to integrate with real-time grid operation data. Although digital twin technology has been applied in the industrial field, there is still a lack of mature solutions for its deep integration with dynamic carbon modeling, real-time IoT data streams, and artificial intelligence predictive models to support high-frequency carbon emission tracking and optimization at the distribution network level.

[0003] Therefore, in the field of carbon emissions, how to measure the carbon emissions of power generation units in real time by constructing a dynamic carbon factor model, and how to fairly distribute carbon emission responsibilities based on flow calculations have become urgent issues to be resolved. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, and medium for carbon emission measurement and responsibility allocation in a distribution network to solve the following technical problems: in the field of carbon emissions, how to measure the carbon emissions of power generation units in real time by constructing a dynamic carbon factor model, and how to fairly allocate carbon emission responsibilities based on power flow calculations.

[0005] In a first aspect, an embodiment of the present application provides a method, device and medium for carbon emission metering and responsibility allocation in a distribution network, the method comprising: real-time collection of multi-source carbon emission data, and pre-processing of the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combining the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; performing flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, and combining the dynamic carbon intensity vector, the node active flux matrix and the branch flow distribution matrix to obtain the total carbon potential of each node on the load side; based on the total carbon potential and the real-time load distribution matrix, calculating the carbon flow rate allocated to each node on the load side to obtain the carbon emission responsibility of each node on the load side.

[0006] In one implementation of the present application, the dynamic carbon factor model is represented by the following formula:

[0007] in, represents the dynamic carbon factor of thermal power units, Indicates the fixed carbon emission coefficient of thermal power units maintaining standby status, It indicates the percentage of carbon intensity coefficient of thermal power generator to rated power. The carbon intensity coefficient of the generator power increase per unit time is expressed as: is the actual power of the thermal power generator, is the rated power of the thermal power generator, It is the power enhancement rate of the thermal power generator per unit time.

[0008] In one implementation of the present application, the dynamic carbon factor of the thermal power unit is combined with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector, specifically including: normalizing the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system; and weightedly fusing the normalized dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system according to the power generation ratio of the thermal power unit and the photovoltaic power generation system to obtain a dynamic carbon intensity vector.

[0009] In one implementation of the present application, a flow calculation is performed on multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, specifically including: performing a flow calculation on multi-source carbon emission data to obtain the voltage amplitude, phase angle and branch active power data of each node on the load side; based on the voltage amplitude, phase angle and branch active power data, calculating the net active injection power of each node on the load side, and combining the net active injection power in sequence to obtain a node active flux matrix; constructing a branch flow distribution matrix based on the node active flux matrix, branch active power data and the grid topology association.

[0010] In one implementation of the present application, the dynamic carbon intensity vector is combined with the node active flux matrix and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side, which is expressed by the following formula:

[0011] in, Indicates the number of load side nodes, represents the carbon potential of all nodes on the load side, represents the node active flux matrix, represents the transpose of the branch power flow distribution matrix, represents the transpose of the thermal power unit injection distribution matrix, where the thermal power unit injection distribution matrix consists of the connection relationship between the thermal power unit and the power system and the active power injected by the unit into the power system. represents the dynamic carbon intensity vector.

[0012] In one implementation of the present application, multi-source carbon emission data is collected in real time, and the multi-source carbon emission data is preprocessed to obtain standard carbon emission data, specifically including: based on sensors deployed on the distribution network side, real-time monitoring and collection of operating parameters of generator sets on the distribution network side and line active power loss data; based on sensors deployed on the load side, real-time collection of energy consumption and environmental parameter data of each node; time alignment and standardization of the operating parameters of generators on the distribution network side and the energy consumption data of each node on the load side to generate standard carbon emission data.

[0013] In one implementation of the present application, based on the total carbon potential and the real-time load distribution matrix, the carbon flow rate allocated to each node on the load side is calculated to obtain the carbon emission responsibility of each node on the load side, specifically including: performing a point multiplication operation on the total carbon potential and the real-time load distribution matrix to obtain the carbon flow rate vector of each node on the load side; based on the carbon flow rate vector, outputting the carbon emission responsibility of each node on the load side.

[0014] In one implementation of the present application, after obtaining the carbon emission responsibility of each node on the load side, the method also includes: tracing the carbon flow source path of the high-carbon load node based on the carbon emission responsibility; establishing a carbon potential peak period early warning mechanism based on the carbon flow source path, and optimizing the scheduling strategy of the generator on the distribution network side and the needs of each node on the load side to generate load adjustment suggestions.

[0015] In a second aspect, an embodiment of the present application also provides a distribution network carbon emission metering and responsibility allocation device, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: collect multi-source carbon emission data in real time, and pre-process the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; perform flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, and combine the dynamic carbon intensity vector, the node active flux matrix and the branch flow distribution matrix to obtain the total carbon potential of each node on the load side; based on the total carbon potential and the real-time load distribution matrix, calculate the carbon flow rate allocated to each node on the load side to obtain the carbon emission responsibility of each node on the load side.

[0016] On the third aspect, an embodiment of the present application also provides a non-volatile computer storage medium for distribution network carbon emission metering and responsibility allocation, which stores computer executable instructions, and the computer executable instructions are set to: collect multi-source carbon emission data in real time, and pre-process the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; perform flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, and combine the dynamic carbon intensity vector, the node active flux matrix and the branch flow distribution matrix to obtain the total carbon potential of each node on the load side; based on the total carbon potential and the real-time load distribution matrix, calculate the carbon flow rate allocated to each node on the load side to obtain the carbon emission responsibility of each node on the load side.

[0017] The embodiments of the present application provide a method, device, and medium for carbon emission metering and responsibility allocation in a distribution network, which have the following beneficial effects: by deploying sensors to collect multi-source real-time data such as thermal power unit power, load energy consumption, and environmental data, and constructing a dynamic carbon factor model for the thermal power unit, which is integrated with the fixed photovoltaic carbon factor to form a dynamic carbon intensity vector, thereby providing a dynamic, multi-dimensional carbon intensity benchmark for carbon flow tracking; by calculating the node active flux matrix and branch flow distribution through a power flow algorithm, and combining the dynamic carbon intensity vector to generate the node carbon potential, high-precision real-time tracking of carbon emissions in the distribution network and fair responsibility allocation are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a method for carbon emission measurement and responsibility allocation in a distribution network provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a distribution network carbon emission metering and responsibility allocation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiments of the present application provide a method, device, and medium for carbon emission measurement and responsibility allocation in a distribution network to solve the following technical problems: in the field of carbon emissions, how to measure the carbon emissions of power generation units in real time by constructing a dynamic carbon factor model, and how to fairly allocate carbon emission responsibilities based on power flow calculations.

[0021] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flow chart of a method for carbon emission measurement and responsibility allocation in a distribution network provided in an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a method for carbon emission measurement and responsibility allocation in a distribution network, which specifically includes the following steps: Step 10: Collect multi-source carbon emission data in real time and pre-process the multi-source carbon emission data to obtain standard carbon emission data.

[0023] As an optional embodiment, multi-source carbon emission data is collected in real time, and the multi-source carbon emission data is preprocessed to obtain standard carbon emission data, which may specifically include: Step 101: Based on sensors deployed on the distribution network side, real-time monitoring and collection of operating parameters of generator sets on the distribution network side and line active power loss data.

[0024] In this step, an IoT sensor network is deployed on the distribution network side. This network is responsible for real-time monitoring and data collection of the operating status of the generator sets and the power loss of the lines on the distribution network side. The sensors will continuously track key operating parameters such as the actual operating power of the generator sets and the power change rate per unit time. These parameters can directly reflect the current power generation level and power regulation dynamics of the generator sets. At the same time, the sensors will also collect real-time active power loss data generated by the distribution network lines during the power transmission process, providing basic data support for the subsequent accurate calculation of the distribution network's carbon emissions.

[0025] Step 102: Based on the sensors deployed on the load side, the energy consumption and environmental parameter data of each node are collected in real time.

[0026] In this step, corresponding sensor equipment is also deployed on the load side to realize real-time data collection of energy consumption of each load node and surrounding environment status. From the energy consumption data dimension, through smart meters and other sensor equipment, real-time collection of energy consumption information of the building as a whole and different areas is carried out to clearly grasp the distribution and change rules of power consumption of each load node; from the environmental parameter dimension, the indoor temperature, humidity, Concentration and other indicators, while using indoor infrared sensors to capture personnel activity data. These environmental parameters and personnel activity data can not only provide a basis for analyzing the influencing factors of load energy consumption changes, but also lay a data foundation for the subsequent optimization of building energy equipment operation strategies.

[0027] Step 103: Time-align and standardize the operating parameters of the generators on the distribution network side and the energy consumption data of each node on the load side to generate standard carbon emission data.

[0028] In this step, first, in order to address the asynchronous problem existing in the operating parameters of the generators on the distribution network side and the energy consumption data of each node on the load side, linear interpolation is used to uniformly adjust these data to a fixed time interval to achieve data alignment in the time dimension, ensuring that the data used for subsequent calculations and analysis have a consistent time base; then, the time-aligned operating parameters of the generators on the distribution network side and the energy consumption data of each node on the load side are converted from the original data format to a unified semantic format, and the converted data are timestamped to clarify the specific time corresponding to the data. At the same time, data cleaning is carried out to eliminate invalid and abnormal data to ensure the accuracy and reliability of the data; through the above-mentioned time alignment and standardization processing flow, the operating parameters of the generators on the distribution network side and the energy consumption data of each node on the load side, which originally had different formats and were not synchronized in time, are converted into standardized data with a unified format, consistent time and reliable quality, providing high-quality input for subsequent dynamic carbon factor modeling, carbon flow allocation calculation and other links, and laying a solid data foundation for real-time measurement and responsibility allocation of carbon emissions in the distribution network.

[0029] Step 20: Input the standard carbon emission data into the preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain the dynamic carbon intensity vector.

[0030] As an optional embodiment, standard carbon emission data is input into a preset dynamic carbon factor model to obtain a dynamic carbon factor of the thermal power unit, and the dynamic carbon factor of the thermal power unit is combined with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector. Specifically, the following steps are included: Step 201: A dynamic carbon factor model is represented by the following formula:

[0031] in, represents the dynamic carbon factor of thermal power units, Indicates the fixed carbon emission coefficient of thermal power units maintaining standby status, It indicates the percentage of carbon intensity coefficient of thermal power generator to rated power. The carbon intensity coefficient of the generator power increase per unit time is expressed as: is the actual power of the thermal power generator, is the rated power of the thermal power generator, It is the power enhancement rate of the thermal power generator per unit time.

[0032] In this step, the dynamic carbon factor model uses mathematical formulas to characterize the real-time carbon emission intensity of thermal power units. Represents the dynamic carbon factor of thermal power units, The fixed carbon emission coefficient required for the thermal power unit to maintain standby status is used to quantify the basic carbon emissions of the unit under the lowest operating state; The carbon intensity coefficient of thermal power generators is the percentage of rated power, reflecting the changing relationship of carbon intensity of generators under different power generation ratios; The carbon intensity coefficient of the generator power increase per unit time reflects the impact of the power regulation rate on carbon emissions; It is the real-time power of the thermal power generator during actual operation, which is directly related to the actual power generation level of the current unit; The rated power of the generator of the thermal power unit is used as a benchmark to measure the relative level of actual power; It represents the power enhancement rate of the thermal power unit generator per unit time, and is used to capture the carbon emission change trend during the dynamic adjustment of the unit's power generation. By substituting the real-time operating parameters of the thermal power unit in the standard carbon emission data into the formula, the dynamic carbon factor of the thermal power unit in the current operating state can be accurately calculated. The dynamic carbon factor model structure comprehensively considers the basic emissions of the unit maintaining standby status, the load level of actual power generation relative to the rated power, and the impact of the power change rate per unit time on carbon intensity.

[0033] Step 202: normalize the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system.

[0034] In this step, after completing the calculation of the dynamic carbon factor of the thermal power unit and the determination of the carbon factor of the photovoltaic power generation system, the two types of carbon factors must first be normalized to eliminate the differences in numerical dimensions between the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system, ensuring that the two are within a unified comparison and calculation benchmark range, laying the foundation for subsequent weighted fusion, avoiding interference with the fusion results due to different original numerical magnitudes, and ensuring the rationality and accuracy of subsequent calculations.

[0035] Step 203: Based on the power generation ratio of the thermal power unit and the photovoltaic power generation system, the normalized dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system are weightedly fused to obtain a dynamic carbon intensity vector.

[0036] In this step, after the normalization process is completed, the two normalized carbon factors are weighted and integrated according to the actual power generation ratio of thermal power units and photovoltaic power generation systems in the current distribution network. The power generation ratio of thermal power units is used as the weight of its normalized dynamic carbon factor, and the power generation ratio of photovoltaic power generation systems is used as the weight of its normalized carbon factor. Through weighted calculation, the two types of carbon factors are integrated into a comprehensive indicator that can reflect the overall carbon emission intensity level of the current distribution network, namely the dynamic carbon intensity vector. This vector will be updated in real time with the changes in the power generation ratio of thermal power units and photovoltaic power generation systems, accurately reflecting the impact of the distribution network power generation structure adjustment on the overall carbon intensity, providing the core carbon intensity basis for the subsequent real-time measurement of carbon emissions, carbon flow allocation and responsibility accounting of the distribution network, and synchronized to the carbon flow allocation module through the data pipeline of the digital twin platform to support the implementation of subsequent work.

[0037] Step 30: Perform power flow calculation on the multi-source carbon emission data to obtain the node active flux matrix and the branch power flow distribution matrix, and combine the dynamic carbon intensity vector, the node active flux matrix and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side.

[0038] As an optional embodiment, a flow calculation is performed on multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, and the dynamic carbon intensity vector, the node active flux matrix and the branch flow distribution matrix are combined to obtain the total carbon potential of each node on the load side. Specifically, it may include: Step 301: performing a flow calculation on multi-source carbon emission data to obtain the voltage amplitude, phase angle and branch active power data of each node on the load side.

[0039] In this step, after completing the preliminary work such as collecting, time-aligning and standardizing multi-source carbon emission-related data, as well as dynamic carbon factor modeling, flow calculations are carried out based on these multi-source carbon emission data to obtain the key electrical parameters of each node on the load side. The power flow calculation process fully utilizes multi-source information such as the operating parameters and line parameters of the generator sets collected on the distribution network side, as well as the energy consumption data of each node on the load side. Through the Newton-Raphson power flow calculation method, the operating status of the distribution network is accurately solved. Through this power flow calculation, the flow pattern of electric energy in the distribution network can be clearly understood, and the voltage amplitude, phase angle and branch active power data of each node on the load side can be output. Among them, the voltage amplitude and phase angle data of each node on the load side reflect the power quality and operating stability of each node, and are an important basis for evaluating the power supply reliability of the distribution network. The branch active power data intuitively presents the power transmission loss and power distribution on each line of the distribution network. These data not only provide the core input for the subsequent construction of the node active flux matrix and the branch power flow distribution matrix, but also lay the key electrical parameter foundation for further carbon flow tracking calculation, determining the node carbon potential and realizing the carbon flow responsibility allocation, ensuring that the subsequent carbon flow-related analysis and calculation can be closely integrated with the actual operating status of the distribution network, and improving the accuracy of carbon emission measurement and responsibility allocation of the distribution network.

[0040] Step 302: Based on the voltage amplitude, phase angle and branch active power data, calculate the net active injection power of each node on the load side, and combine the net active injection power in sequence to obtain a node active flux matrix.

[0041] In this step, after obtaining the voltage amplitude, phase angle and branch active power data of each node on the load side through flow calculation, the net active injection power of each node on the load side is further calculated based on these electrical parameters. Taking the node as the unit, the active power exchange between the node and the adjacent branch is comprehensively considered, including the active power input from the outside to the node, and also covering the active power output from the node to the outside. The net active injection power of each load-side node is obtained by calculating the difference between the input and output active powers. This power value can accurately reflect the actual active power income and expenditure status of a single node in the process of power flow in the distribution network. After completing the calculation of the net active injection power of all load-side nodes, according to the preset The node numbering order or node arrangement logic in the distribution network topology is used to combine the net active injection power of each node in sequence, thereby forming a node active flux matrix that can comprehensively characterize the active power injection situation of all load-side nodes in the distribution network. This matrix not only clearly presents the net active injection scale of each load-side node, but also reflects the position relationship and power distribution characteristics of each node in the distribution network through orderly arrangement, providing structured active power data support for subsequent node carbon potential calculation, carbon flow allocation and load-side carbon responsibility accounting based on carbon flow theory, ensuring that carbon flow-related calculations can accurately match the actual power flow status of the distribution network, and improving the accuracy of carbon emission measurement and responsibility allocation.

[0042] Step 303: constructing a branch power flow distribution matrix based on the node active flux matrix, branch active power data and grid topology association.

[0043] In this step, after obtaining the node active flux matrix and branch active power data, and clarifying the grid topology association, the branch flow distribution matrix is ​​constructed based on this. First, the grid topology association clearly defines the connection logic of each node and branch in the distribution network, clarifies the starting node and the ending node of each branch, and provides a structural basis for determining the direction and ownership of branch power flow. Combined with the branch active power data, the actual active power transmitted by each branch in the current operating state can be obtained, and the node active flux matrix can assist in verifying the balance between branch power and node net active injection power to ensure data consistency and accuracy; when constructing the branch flow distribution matrix, according to the preset branch numbering rules or grid The logical order of the topology integrates the key information corresponding to each branch in an orderly manner. The rows and columns of the matrix are usually associated with branch identification and node information respectively. The core content of each branch, such as the starting node, ending node, transmitted active power value and power flow direction, is clearly recorded through matrix elements. This structured matrix form can not only intuitively present the overall branch power distribution of the entire distribution network, but also accurately reflect the power interaction relationship between branches and nodes. It provides key branch-level power distribution data support for subsequent carbon flow tracking calculations based on carbon flow theory, determination of the carbon flow transmission intensity of each branch, and ultimate realization of accurate measurement and responsibility allocation of carbon emissions in the distribution network, ensuring that the carbon flow analysis is highly consistent with the actual power transmission status of the distribution network.

[0044] Step 304: Combine the dynamic carbon intensity vector with the node active flux matrix and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side, which is expressed by the following formula:

[0045] in, Indicates the number of load side nodes, represents the carbon potential of all nodes on the load side, represents the node active flux matrix, represents the transpose of the branch power flow distribution matrix, represents the transpose of the thermal power unit injection distribution matrix, where the thermal power unit injection distribution matrix consists of the connection relationship between the thermal power unit and the power system and the active power injected by the unit into the power system. represents the dynamic carbon intensity vector.

[0046] In this step, after completing the construction of the dynamic carbon intensity vector, node active flux matrix and branch flow distribution matrix, these three types of core data are combined and calculated through a specific formula to obtain the total carbon potential of each node on the load side. Among them, the node active flux matrix Clearly presents the net active injection power distribution of each node on the load side and the transpose of the branch power flow distribution matrix From the reverse dimension of branch power transmission, the power interaction relationship between nodes and branches is supplemented. Together, the two constitute the basic data framework that reflects the power flow structure of the distribution network. Dynamic carbon intensity vector As a system-level carbon intensity benchmark, it includes the dynamic carbon factor of thermal power units and the carbon factor of photovoltaic systems, quantifying the carbon emission characteristics of different units; and the transpose of the injection distribution matrix of thermal power units Based on the connection relationship between the thermal power units and the power system and the actual active power injected by the units, the specific distribution of the power delivered by each thermal power unit is clarified, providing a power distribution basis for the transfer of carbon flow from the power generation side to the load side; in calculating the total carbon potential of each node on the load side When we first perform matrix operations A matrix that can characterize the node power balance relationship is constructed. This matrix essentially reflects the net power state of the load-side node after deducting the influence of branch power interaction. The matrix is ​​then inverted and then injected into the transpose of the distribution matrix of the thermal power unit. and dynamic carbon intensity vector Perform matrix multiplication operations in sequence. Through this series of operations, the carbon emission intensity of the power generation side is converted into Based on power injection distribution and distribution network power flow structure , accurately distributed to each node on the load side, and finally the carbon potential of all nodes on the load side is obtained It can dynamically reflect the carbon emission intensity of each unit of electricity consumed by different load nodes in the distribution network, and provide a key node-level carbon emission basis for subsequent load-side carbon responsibility accounting.

[0047] Step 40: Based on the total carbon potential and the real-time load distribution matrix, calculate the carbon flow rate allocated to each node on the load side to obtain the carbon emission responsibility of each node on the load side.

[0048] As an optional embodiment, based on the total carbon potential and the real-time load distribution matrix, the carbon flow rate allocated to each node on the load side is calculated to obtain the carbon emission responsibility of each node on the load side. Specifically, it may include: Step 401: Perform a point multiplication operation on the total carbon potential and the real-time load distribution matrix to obtain the carbon flow rate vector of each node on the load side.

[0049] In this step, the total carbon potential serves as the core parameter for characterizing carbon emission intensity. Each element in its matrix corresponds to the carbon potential level of a single node on the load side, and is directly related to the carbon emission responsibility density corresponding to the electricity consumption behavior of that node. The real-time load distribution matrix focuses on the actual scale of electricity demand. The matrix elements accurately reflect the current active power consumption values ​​of each node, reflecting the differences in electricity load at different nodes. During the dot product operation, the total carbon potential matrix is ​​multiplied one by one with the elements of the corresponding node in the real-time load distribution matrix. That is, the carbon potential value of each node is multiplied by the real-time active power consumption value of the node. The result is the carbon emissions generated by the node per unit time, that is, the carbon flow rate of a single node. The carbon flow rates calculated in this way for all nodes are integrated in node order to form a carbon flow rate vector that can comprehensively reflect the carbon emission rate of each node on the load side. This vector not only clearly presents the carbon emission contribution of each load node, but also provides an accurate quantitative basis for subsequently clarifying the carbon responsibility attribution on the load side, conducting carbon flow tracking, and formulating targeted emission reduction strategies.

[0050] Step 402: Based on the carbon flow rate vector, output the carbon emission responsibility of each node on the load side.

[0051] In this step, after calculating the carbon flow rate vector for each load-side node, the carbon emission responsibility of each node is output using this vector as the core basis. Each element in the carbon flow rate vector corresponds to the carbon flow rate of a single load-side node. Essentially, it represents the carbon emissions generated by that node's actual electricity consumption per unit time. This directly quantifies the node's contribution to the distribution network's carbon emissions. Since the carbon flow rate vector is obtained by multiplying the total carbon potential by the real-time load distribution matrix, its result fully incorporates the dual dimensions of node electricity consumption intensity and scale, accurately reflecting the differences in carbon emission responsibility resulting from differences in electricity consumption characteristics at different nodes. Therefore, by interpreting the numerical values ​​of each element in the carbon flow rate vector, the carbon emission responsibility share of each load-side node can be clearly divided. Nodes with higher carbon flow rate values ​​have heavier corresponding carbon emission responsibilities, and vice versa. These clear carbon emission responsibility division results provide a clear basis for tracing the source of carbon flow at high-carbon load nodes and formulating targeted emission reduction strategies. This helps distribution network operators and load-side users clearly understand their own carbon responsibility status and provides data support for low-carbon decision-making.

[0052] Step 403: Based on carbon emission responsibility, trace the carbon flow source path of the high carbon load node.

[0053] In this step, after clarifying the carbon emission responsibility of each node on the load side, for the high-carbon load nodes with higher values ​​in the carbon flow rate vector and heavier carbon emission responsibility, the carbon flow source path tracing work is carried out in combination with the topological relationship of the distribution network, the branch flow distribution matrix and the carbon flow tracing logic. First, the topological relationship of the power grid clearly defines the connection structure between the high-carbon load nodes and the surrounding branches, adjacent nodes and generators, and clarifies the physical path framework of power transmission; the branch flow distribution matrix provides the active power transmission direction and size of each branch, which can reflect the specific context of the flow of electric energy from the power generation side to the load side, and provides a quantitative basis for tracing the carbon flow transmission path; in the tracing process, the high-carbon load nodes are used to Taking the load node as the starting point, the transmission path of electrical energy is reversely deduced based on the branch flow distribution matrix to determine through which branches the electricity consumed by the node is transmitted from the upstream node, and then the source of electricity of the upstream node is further traced until the generator set providing electricity is located. At the same time, combined with the carbon factor characteristics of each unit in the dynamic carbon intensity vector, the proportion of electricity consumed by the high-carbon load node from different types of generator sets can be simultaneously clarified; through this series of tracing steps, a complete carbon flow source path of the high-carbon load node is finally formed, which clearly presents the source of the power generation end and the electricity transmission process corresponding to the carbon emission responsibility of the node, providing an accurate path basis for the subsequent analysis of the causes of high carbon emissions and the formulation of targeted emission reduction strategies.

[0054] Step 404: Based on the carbon flow source path, establish a carbon potential peak period warning mechanism, and optimize the dispatching strategy of the generator on the distribution network side and the demand of each node on the load side to generate load adjustment suggestions.

[0055] In this step, after completing the carbon flow source path tracing of the high-carbon load node, based on the path information, combined with the dynamic carbon factor change law, historical carbon potential data and real-time operating parameters, a carbon potential peak period warning mechanism is constructed. First, by analyzing the carbon factor fluctuation characteristics of each generator set in the carbon flow source path, the weight of the power generation changes of different generator sets on the overall carbon potential of the distribution network is clarified; at the same time, combined with the operating data of the carbon potential peak period in the same historical period, the correlation law between the increase in carbon potential and the scheduling and load changes of generator sets is explored. On this basis, the pre-deployed lightweight artificial neural network will input the real-time collected temperature, load power and other data to predict the carbon potential change trend of the distribution network in the future. When it is predicted that the carbon potential will reach the set threshold, the warning will be automatically triggered, and the warning information will be pushed to the distribution network operator and the load-side users in time to reserve time for subsequent adjustments; under the support of the warning mechanism, the distribution network will be further optimized. The dispatching strategy of grid-side generators gives priority to dispatching generators with lower carbon factors according to the dynamic carbon factors of each thermal power unit in the carbon flow source path and the carbon factor differences of the photovoltaic system. During the period of sufficient photovoltaic power generation, the power generation proportion of thermal power units with high carbon factors is appropriately reduced, and the carbon factors of thermal power units are updated in real time through the dynamic carbon factor model to reduce total carbon emissions. At the same time, the transmission path of power generation is adjusted in combination with the branch flow distribution to ensure that low-carbon generators give priority to supplying high-carbon potential nodes and reduce the carbon potential of nodes. For each node on the load side, load adjustment suggestions are generated based on the causes of node carbon emissions and real-time load distribution reflected in the carbon flow source path. During the peak carbon potential period, it is recommended to reduce non-essential loads such as air conditioning and lighting for high-carbon load nodes to reduce their dependence on high-carbon generators. During the peak photovoltaic power generation and low carbon potential period, the nodes on the load side are guided to give priority to the use of electric drive equipment to improve the absorption rate of low-carbon generators.

[0056] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a distribution network carbon emission measurement and responsibility allocation device, the structure of which is as follows: Figure 2 shown.

[0057] Figure 2 This is a schematic diagram of the internal structure of a distribution network carbon emission measurement and responsibility allocation device provided in an embodiment of the present application. Figure 2 As shown, the equipment includes: at least one processor 201; and, a memory 202 communicatively coupled to the at least one processor; Among them, the memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 so that the at least one processor 201 can: collect multi-source carbon emission data in real time, and pre-process the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; perform flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, and combine the dynamic carbon intensity vector, the node active flux matrix and the branch flow distribution matrix to obtain the total carbon potential of each node on the load side; based on the total carbon potential and the real-time load distribution matrix, calculate the carbon flow rate allocated to each node on the load side to obtain the carbon emission responsibility of each node on the load side.

[0058] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for carbon emission measurement and responsibility allocation in a distribution network stores computer executable instructions, wherein the computer executable instructions are configured to: collect multi-source carbon emission data in real time, and pre-process the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain a dynamic carbon factor of a thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of a photovoltaic power generation system to obtain a dynamic carbon intensity vector; perform a flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch flow distribution matrix, and combine the dynamic carbon intensity vector, the node active flux matrix, and the branch flow distribution matrix to obtain a total carbon potential of each node on the load side; and calculate the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

[0059] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0060] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0067] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0069] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for measuring carbon emissions and allocating responsibilities in a distribution network, characterized in that: The method comprises: Collect multi-source carbon emission data in real time and pre-process the multi-source carbon emission data to obtain standard carbon emission data; wherein the multi-source carbon emission data includes operating parameters of generators on the distribution network side and energy consumption data of each node on the load side; Inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain a dynamic carbon factor for the thermal power unit, and combining the dynamic carbon factor for the thermal power unit with the carbon factor for the photovoltaic power generation system to obtain a dynamic carbon intensity vector; wherein the carbon factor for the photovoltaic power generation system is a fixed value calculated using an internationally recognized life cycle assessment method; Performing a power flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch power flow distribution matrix, and combining the dynamic carbon intensity vector, the node active flux matrix, and the branch power flow distribution matrix to obtain a total carbon potential for each node on the load side; wherein the node active flux matrix includes carbon flow sources and carbon flow sinks; and the branch power flow distribution matrix is ​​used to achieve carbon flow path tracking; Based on the total carbon potential and the real-time load distribution matrix, the carbon flow rate allocated to each node on the load side is calculated to obtain the carbon emission responsibility of each node on the load side.

2. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: The method further comprises: The dynamic carbon factor model is represented by the following formula: in, represents the dynamic carbon factor of thermal power units, Indicates the fixed carbon emission coefficient of thermal power units maintaining standby status, It indicates the percentage of carbon intensity coefficient of thermal power generator to rated power. The carbon intensity coefficient of the generator power increase per unit time is expressed as: is the actual power of the thermal power generator, is the rated power of the thermal power generator, It is the power enhancement rate of the thermal power generator per unit time.

3. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: The dynamic carbon factor of the thermal power unit is combined with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector, specifically including: Normalizing the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system; According to the power generation ratio of the thermal power unit and the photovoltaic power generation system, the normalized dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system are weightedly fused to obtain the dynamic carbon intensity vector.

4. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: Performing power flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch power flow distribution matrix, specifically including: Performing power flow calculation on the multi-source carbon emission data to obtain voltage amplitude, phase angle and branch active power data of each node on the load side; Based on the voltage amplitude, the phase angle and the branch active power data, the net active injection power of each node on the load side is calculated, and the net active injection power is combined in sequence to obtain the node active flux matrix; The branch power flow distribution matrix is ​​constructed based on the node active flux matrix, the branch active power data and the grid topology association relationship.

5. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: The dynamic carbon intensity vector, the node active flux matrix, and the branch power flow distribution matrix are combined to obtain the total carbon potential of each node on the load side, which is expressed by the following formula: in, Indicates the number of load side nodes, represents the carbon potential of all nodes on the load side, represents the node active flux matrix, represents the transpose of the branch power flow distribution matrix, represents the transpose of the thermal power unit injection distribution matrix, where the thermal power unit injection distribution matrix consists of the connection relationship between the thermal power unit and the power system and the active power injected by the unit into the power system. represents the dynamic carbon intensity vector.

6. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: Real-time collection of multi-source carbon emission data and pre-processing of the multi-source carbon emission data to obtain standard carbon emission data, specifically including: Based on sensors deployed on the distribution network side, real-time monitoring and collection of operating parameters of the generator sets on the distribution network side and line active power loss data; Based on sensors deployed on the load side, energy consumption and environmental parameter data of each node are collected in real time; Time alignment and standardization are performed on the operating parameters of the generator on the distribution network side and the energy consumption data of each node on the load side to generate the standard carbon emission data.

7. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: Based on the total carbon potential and the real-time load distribution matrix, the carbon flow rate allocated to each node on the load side is calculated to obtain the carbon emission responsibility of each node on the load side, specifically including: Performing a point multiplication operation on the total carbon potential and the real-time load distribution matrix to obtain a carbon flow rate vector of each node on the load side; Based on the carbon flow rate vector, the carbon emission responsibility of each node on the load side is output.

8. A method for carbon emission measurement and responsibility allocation in a distribution network according to claim 1, characterized in that: After obtaining the carbon emission responsibility of each node on the load side, the method further includes: Based on the carbon emission responsibility, trace the carbon flow source path of high carbon load nodes; Based on the carbon flow source path, an early warning mechanism for carbon potential peak period is established, and the dispatching strategy of the generator on the distribution network side and the demand of each node on the load side are optimized to generate load adjustment suggestions.

9. A distribution network carbon emission measurement and responsibility allocation device, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A non-volatile computer storage medium for carbon emission measurement and responsibility allocation in a distribution network, storing computer-executable instructions, characterized in that: When the computer-executable instructions are executed, the method according to any one of claims 1 to 8 is implemented.

Images